1
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Zhao Z, Shuai Y, Wu Y, Xu X, Li M, Wu D. Age-dependent functional development pattern in neonatal brain: An fMRI-based brain entropy study. Neuroimage 2024; 297:120669. [PMID: 38852805 DOI: 10.1016/j.neuroimage.2024.120669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 04/01/2024] [Accepted: 06/05/2024] [Indexed: 06/11/2024] Open
Abstract
The relationship between brain entropy (BEN) and early brain development has been established through animal studies. However, it remains unclear whether the BEN can be used to identify age-dependent functional changes in human neonatal brains and the genetic underpinning of the new neuroimaging marker remains to be elucidated. In this study, we analyzed resting-state fMRI data from the Developing Human Connectome Project, including 280 infants who were scanned at 37.5-43.5 weeks postmenstrual age. The BEN maps were calculated for each subject, and a voxel-wise analysis was conducted using a general linear model to examine the effects of age, sex, and preterm birth on BEN. Additionally, we evaluated the correlation between regional BEN and gene expression levels. Our results demonstrated that the BEN in the sensorimotor-auditory and association cortices, along the 'S-A' axis, was significantly positively correlated with postnatal age (PNA), and negatively correlated with gestational age (GA), respectively. Meanwhile, the BEN in the right rolandic operculum correlated significantly with both GA and PNA. Preterm-born infants exhibited increased BEN values in widespread cortical areas, particularly in the visual-motor cortex, when compared to term-born infants. Moreover, we identified five BEN-related genes (DNAJC12, FIG4, STX12, CETN2, and IRF2BP2), which were involved in protein folding, synaptic vesicle transportation and cell division. These findings suggest that the fMRI-based BEN can serve as an indicator of age-dependent brain functional development in human neonates, which may be influenced by specific genes.
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Affiliation(s)
- Zhiyong Zhao
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Yifan Shuai
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yihan Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
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2
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Pujol J, Blanco-Hinojo L, Persavento C, Martínez-Vilavella G, Falcón C, Gascón M, Rivas I, Vilanova M, Deus J, Gispert JD, Gómez-Roig MD, Llurba E, Dadvand P, Sunyer J. Functional structure of local connections and differentiation of cerebral cortex areas in the neonate. Neuroimage 2024; 298:120780. [PMID: 39122060 DOI: 10.1016/j.neuroimage.2024.120780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/16/2024] [Accepted: 08/07/2024] [Indexed: 08/12/2024] Open
Abstract
Neuroimaging research on functional connectivity can provide valuable information on the developmental differentiation of the infant cerebral cortex into its functional areas. We examined healthy neonates to comprehensively map brain functional connectivity using a combination of local measures that uniquely capture the rich spatial structure of cerebral cortex functional connections. Optimal functional MRI scans were obtained in 61 neonates. Local functional connectivity maps were based on Iso-Distance Average Correlation (IDAC) measures. Single distance maps and maps combining three distinct IDAC measures were used to assess different levels of cortical area functional differentiation. A set of brain areas showed higher connectivity than the rest of the brain parenchyma in each local distance map. These areas were consistent with those supporting basic aspects of the neonatal repertoire of adaptive behaviors and included the sensorimotor, auditory and visual cortices, the frontal operculum/anterior insula (relevant for sucking, swallowing and the sense of taste), paracentral lobule (processing anal and urethral sphincter activity), default mode network (relevant for self-awareness), and limbic-emotional structures such as the anterior cingulate cortex, amygdala and hippocampus. However, the results also indicate that brain areas presumed to be actively developing may not necessarily be mature. In fact, combined distance, second-level maps confirmed that the functional differentiation of the cerebral cortex into functional areas in neonates is far from complete. Our results provide a more comprehensive understanding of the developing brain systems, while also highlighting the substantial developmental journey that the neonatal brain must undergo to reach adulthood.
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Affiliation(s)
- Jesus Pujol
- MRI Research Unit, Department of Radiology, Hospital del Mar, Passeig Marítim 25-29, Barcelona 08003, Spain.
| | - Laura Blanco-Hinojo
- MRI Research Unit, Department of Radiology, Hospital del Mar, Passeig Marítim 25-29, Barcelona 08003, Spain; ISGlobal, Barcelona, Spain
| | - Cecilia Persavento
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Gerard Martínez-Vilavella
- MRI Research Unit, Department of Radiology, Hospital del Mar, Passeig Marítim 25-29, Barcelona 08003, Spain
| | - Carles Falcón
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; CIBER Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Mireia Gascón
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Ioar Rivas
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Marc Vilanova
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Joan Deus
- MRI Research Unit, Department of Radiology, Hospital del Mar, Passeig Marítim 25-29, Barcelona 08003, Spain; Department of Clinical and Health Psychology, Autonomous University of Barcelona, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; CIBER Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Maria Dolors Gómez-Roig
- BCNatal, Fetal Medicine Research Center, Hospital Sant Joan de Déu and Hospital Clínic, University of Barcelona, Barcelona, Spain; Institut de Recerca Sant Joan de Déu, Barcelona, Spain; Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS), RD21/0012/1&3, Instituto de Salud Carlos III, Madrid, Spain
| | - Elisa Llurba
- Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS), RD21/0012/1&3, Instituto de Salud Carlos III, Madrid, Spain; Department of Obstetrics and Gynaecology. Institut d'Investigació Biomèdica Sant Pau - IIB Sant Pau. Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Payam Dadvand
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
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3
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Derman D, Pham DD, Mejia AF, Ferradal SL. Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.24.550218. [PMID: 39149306 PMCID: PMC11326129 DOI: 10.1101/2023.07.24.550218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.
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Affiliation(s)
- Diego Derman
- Department of Intelligent Systems Engineering, Indiana University, USA
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4
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Tu JC, Wang Y, Wang X, Dierker D, Sobolewski CM, Day TKM, Kardan O, Miranda-Domínguez Ó, Moore LA, Elison JT, Gordon EM, Laumann TO, Eggebrecht AT, Wheelock MD. A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.606025. [PMID: 39131337 PMCID: PMC11312607 DOI: 10.1101/2024.07.31.606025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized as functional networks. These areas show stronger internal and weaker external functional connectivity (FC) and exhibit similar FC profiles within rather than between networks. Previous studies have demonstrated the development of these networks from nascent forms present before birth to their mature, adult-like topography in childhood. However, analyses often still use definitions based on adult functional networks. We aim to assess how this might lead to the misidentification of functional networks and explore potential consequences and solutions. Our findings suggest that even though adult networks provide only a marginally better than-chance description of the infant FC organization, misidentification was largely driven by specific areas. By restricting functional networks to areas showing adult-like network clustering, we observed consistent within-network FC both within and across scans and throughout development. Additionally, these areas were spatially closer to locations with low variability in network identity among adults. Our analysis aids in understanding the potential consequences of using adult networks "as is" and provides guidance for future research on selecting and utilizing functional network models based on the research question and scenario.
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Affiliation(s)
| | - Yu Wang
- Department of Mathematics and Statistics, Washington University in St. Louis
| | - Xintian Wang
- Department of Radiology, Washington University in St. Louis
| | - Donna Dierker
- Department of Radiology, Washington University in St. Louis
| | - Chloe M. Sobolewski
- Department of Radiology, Washington University in St. Louis
- Department of Psychology, Virginia Commonwealth University
| | - Trevor K. M. Day
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Center for Brain Plasticity and Recovery, Georgetown University
| | - Omid Kardan
- Department of Psychiatry, University of Michigan
| | | | - Lucille A. Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Jed T. Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
| | - Evan M. Gordon
- Department of Radiology, Washington University in St. Louis
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5
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Harp NR, Wager TD, Kober H. Neuromarkers in addiction: definitions, development strategies, and recent advances. J Neural Transm (Vienna) 2024; 131:509-523. [PMID: 38630190 DOI: 10.1007/s00702-024-02766-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/12/2024] [Indexed: 04/28/2024]
Abstract
Substance use disorders (SUDs) are the most costly and prevalent psychiatric conditions. Recent calls emphasize a need for biomarkers-measurable, stable indicators of normal and abnormal processes and response to treatment or environmental agents-and, in particular, brain-based neuromarkers that will advance understanding of the neurobiological basis of SUDs and clinical practice. To develop neuromarkers, researchers must be grounded in evidence that a putative marker (i) is sensitive and specific to the psychological phenomenon of interest, (ii) constitutes a predictive model, and (iii) generalizes to novel observations (e.g., through internal cross-validation and external application to novel data). These neuromarkers may be used to index risk of developing SUDs (susceptibility), classify individuals with SUDs (diagnostic), assess risk for progression to more severe pathology (prognostic) or index current severity of pathology (monitoring), detect response to treatment (response), and predict individualized treatment outcomes (predictive). Here, we outline guidelines for developing and assessing neuromarkers, we then review recent advances toward neuromarkers in addiction neuroscience centering our discussion around neuromarkers of craving-a core feature of SUDs. In doing so, we specifically focus on the Neurobiological Craving Signature (NCS), which show great promise for meeting the demand of neuromarkers.
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Affiliation(s)
- Nicholas R Harp
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tor D Wager
- Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Hedy Kober
- Department of Psychiatry, Yale University, New Haven, CT, USA.
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6
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Sun H, Mehta S, Khaitova M, Cheng B, Hao X, Spann M, Scheinost D. Brain age prediction and deviations from normative trajectories in the neonatal connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590811. [PMID: 38712238 PMCID: PMC11071351 DOI: 10.1101/2024.04.23.590811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age-or brain age gap (BAG)-represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks-even putatively later developing ones-generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.
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7
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Harp NR, Nielsen AN, Schultz DH, Neta M. In the face of ambiguity: intrinsic brain organization in development predicts one's bias toward positivity or negativity. Cereb Cortex 2024; 34:bhae102. [PMID: 38494885 PMCID: PMC10945044 DOI: 10.1093/cercor/bhae102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 03/19/2024] Open
Abstract
Exacerbated negativity bias, including in responses to ambiguity, represents a common phenotype of internalizing disorders. Individuals differ in their propensity toward positive or negative appraisals of ambiguity. This variability constitutes one's valence bias, a stable construct linked to mental health. Evidence suggests an initial negativity in response to ambiguity that updates via regulatory processes to support a more positive bias. Previous work implicates the amygdala and prefrontal cortex, and regions of the cingulo-opercular system, in this regulatory process. Nonetheless, the neurodevelopmental origins of valence bias remain unclear. The current study tests whether intrinsic brain organization predicts valence bias among 119 children and adolescents (6 to 17 years). Using whole-brain resting-state functional connectivity, a machine-learning model predicted valence bias (r = 0.20, P = 0.03), as did a model restricted to amygdala and cingulo-opercular system features (r = 0.19, P = 0.04). Disrupting connectivity revealed additional intra-system (e.g. fronto-parietal) and inter-system (e.g. amygdala to cingulo-opercular) connectivity important for prediction. The results highlight top-down control systems and bottom-up perceptual processes that influence valence bias in development. Thus, intrinsic brain organization informs the neurodevelopmental origins of valence bias, and directs future work aimed at explicating related internalizing symptomology.
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Affiliation(s)
- Nicholas R Harp
- Department of Psychiatry, Yale University, 300 George Street, New Haven, CT 06511, United States
| | - Ashley N Nielsen
- Department of Neurology, Washington University, 660 S. Euclid Ave., St. Louis, MO 63110, United States
| | - Douglas H Schultz
- Department of Psychology, University of Nebraska-Lincoln, 238 Burnett Hall, Lincoln, NE 68588, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, C89 East Stadium, Lincoln, NE 68588, United States
| | - Maital Neta
- Department of Psychology, University of Nebraska-Lincoln, 238 Burnett Hall, Lincoln, NE 68588, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, C89 East Stadium, Lincoln, NE 68588, United States
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8
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose RF, Camacho MC, Alexopoulos D, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal cortical surface. Cereb Cortex 2024; 34:bhae047. [PMID: 38372292 PMCID: PMC10875653 DOI: 10.1093/cercor/bhae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/20/2024] Open
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Jiaxin C Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Rebecca F Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States
- Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, United States
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO 63110, United States
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9
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose R, Camacho MC, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566629. [PMID: 37986902 PMCID: PMC10659431 DOI: 10.1101/2023.11.10.566629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Jiaxin Cindy Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Rebecca Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO, USA
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10
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Nielsen AN, Graham AM, Sylvester CM. Baby Brains at Work: How Task-Based Functional Magnetic Resonance Imaging Can Illuminate the Early Emergence of Psychiatric Risk. Biol Psychiatry 2023; 93:880-892. [PMID: 36935330 PMCID: PMC10149573 DOI: 10.1016/j.biopsych.2023.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 12/19/2022] [Accepted: 01/12/2023] [Indexed: 01/22/2023]
Abstract
Psychiatric disorders are complex, often emerging from multiple atypical processes within specified domains over the course of development. Characterizing the development of the neural circuits supporting these domains may help break down the components of complex disorders and reveal variations in functioning associated with psychiatric risk. This review highlights the current and potential role of infant task-based functional magnetic resonance imaging (fMRI) in elucidating the developmental neurobiology of psychiatric disorders. Task-fMRI measures evoked brain activity in response to specific stimuli through changes in the blood oxygen level-dependent signal. First, we review extant studies using task fMRI from birth through the first few years of life and synthesize current evidence for when, where, and how different neural computations are performed across the infant brain. Neural circuits for sensory perception, the perception of abstract categories, and the detection of statistical regularities have been characterized with task fMRI in infants, providing developmental context for identifying and interpreting variation in the functioning of neural circuits related to psychiatric risk. Next, we discuss studies that specifically examine variation in the functioning of these neural circuits during infancy in relation to risk for psychiatric disorders. These studies reveal when maturation of specific neural circuits diverges, the influence of environmental risk factors, and the potential utility for task fMRI to facilitate early treatment or prevention of later psychiatric problems. Finally, we provide considerations for future infant task-fMRI studies with the potential to advance understanding of both functioning of neural circuits during infancy and subsequent risk for psychiatric disorders.
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Affiliation(s)
- Ashley N Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri.
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Sciences University, Portland, Oregon
| | - Chad M Sylvester
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
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11
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Sydnor VJ, Larsen B, Seidlitz J, Adebimpe A, Alexander-Bloch AF, Bassett DS, Bertolero MA, Cieslak M, Covitz S, Fan Y, Gur RE, Gur RC, Mackey AP, Moore TM, Roalf DR, Shinohara RT, Satterthwaite TD. Intrinsic activity development unfolds along a sensorimotor-association cortical axis in youth. Nat Neurosci 2023; 26:638-649. [PMID: 36973514 PMCID: PMC10406167 DOI: 10.1038/s41593-023-01282-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/15/2023] [Indexed: 03/29/2023]
Abstract
Animal studies of neurodevelopment have shown that recordings of intrinsic cortical activity evolve from synchronized and high amplitude to sparse and low amplitude as plasticity declines and the cortex matures. Leveraging resting-state functional MRI (fMRI) data from 1,033 youths (ages 8-23 years), we find that this stereotyped refinement of intrinsic activity occurs during human development and provides evidence for a cortical gradient of neurodevelopmental change. Declines in the amplitude of intrinsic fMRI activity were initiated heterochronously across regions and were coupled to the maturation of intracortical myelin, a developmental plasticity regulator. Spatiotemporal variability in regional developmental trajectories was organized along a hierarchical, sensorimotor-association cortical axis from ages 8 to 18. The sensorimotor-association axis furthermore captured variation in associations between youths' neighborhood environments and intrinsic fMRI activity; associations suggest that the effects of environmental disadvantage on the maturing brain diverge most across this axis during midadolescence. These results uncover a hierarchical neurodevelopmental axis and offer insight into the progression of cortical plasticity in humans.
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Affiliation(s)
- Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Azeez Adebimpe
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Maxwell A Bertolero
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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12
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Kopić J, Junaković A, Salamon I, Rasin MR, Kostović I, Krsnik Ž. Early Regional Patterning in the Human Prefrontal Cortex Revealed by Laminar Dynamics of Deep Projection Neuron Markers. Cells 2023; 12:231. [PMID: 36672166 PMCID: PMC9856843 DOI: 10.3390/cells12020231] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/27/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023] Open
Abstract
Early regional patterning and laminar position of cortical projection neurons is determined by activation and deactivation of transcriptional factors (TFs) and RNA binding proteins (RBPs) that regulate spatiotemporal framework of neurogenetic processes (proliferation, migration, aggregation, postmigratory differentiation, molecular identity acquisition, axonal growth, dendritic development, and synaptogenesis) within transient cellular compartments. Deep-layer projection neurons (DPN), subplate (SPN), and Cajal-Retzius neurons (CRN) are early-born cells involved in the establishment of basic laminar and regional cortical architecture; nonetheless, laminar dynamics of their molecular transcriptional markers remain underexplored. Here we aimed to analyze laminar dynamics of DPN markers, i.e., transcription factors TBR1, CTIP2, TLE4, SOX5, and RBP CELF1 on histological serial sections of the human frontal cortex between 7.5-15 postconceptional weeks (PCW) in reference to transient proliferative, migratory, and postmigratory compartments. The subtle signs of regional patterning were seen during the late preplate phase in the pattern of sublaminar organization of TBR1+/Reelin+ CRN and TBR1+ pioneering SPN. During the cortical plate (CP)-formation phase, TBR1+ neurons became radially aligned, forming continuity from a well-developed subventricular zone to CP showing clear lateral to medial regional gradients. The most prominent regional patterning was seen during the subplate formation phase (around 13 PCW) when a unique feature of the orbitobasal frontal cortex displays a "double plate" pattern. In other portions of the frontal cortex (lateral, dorsal, medial) deep portion of CP becomes loose and composed of TBR1+, CTIP2+, TLE4+, and CELF1+ neurons of layer six and later-born SPN, which later become constituents of the expanded SP (around 15 PCW). Overall, TFs and RBPs mark characteristic regional laminar dynamics of DPN, SPN, and CRN subpopulations during remarkably early fetal phases of the highly ordered association cortex development.
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Affiliation(s)
- Janja Kopić
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Salata 12, 10000 Zagreb, Croatia
| | - Alisa Junaković
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Salata 12, 10000 Zagreb, Croatia
| | - Iva Salamon
- Department of Neuroscience and Cell Biology, Robert Wood Johnson Medical School, Rutgers University, 675 Hoes Lane West, Piscataway, NJ 08854, USA
- School of Graduate Studies, Rutgers University, New Brunswick, NJ 08854, USA
| | - Mladen-Roko Rasin
- Department of Neuroscience and Cell Biology, Robert Wood Johnson Medical School, Rutgers University, 675 Hoes Lane West, Piscataway, NJ 08854, USA
| | - Ivica Kostović
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Salata 12, 10000 Zagreb, Croatia
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Salata 12, 10000 Zagreb, Croatia
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